Overview

Dataset statistics

Number of variables30
Number of observations19070
Missing cells0
Missing cells (%)0.0%
Duplicate rows1330
Duplicate rows (%)7.0%
Total size in memory4.4 MiB
Average record size in memory240.0 B

Variable types

Numeric19
Categorical11

Alerts

незачет has constant value ""Constant
Накоп незачет has constant value ""Constant
Dataset has 1330 (7.0%) duplicate rowsDuplicates
Семестр is highly overall correlated with Накоп зачет and 1 other fieldsHigh correlation
удовлетворительно is highly overall correlated with отлично and 1 other fieldsHigh correlation
отлично is highly overall correlated with удовлетворительно and 1 other fieldsHigh correlation
зачет испр is highly overall correlated with незачет до испр and 2 other fieldsHigh correlation
незачет до испр is highly overall correlated with зачет испр and 2 other fieldsHigh correlation
Накоп зачет is highly overall correlated with Семестр and 1 other fieldsHigh correlation
Накоп удовлетворительно is highly overall correlated with удовлетворительноHigh correlation
Накоп хорошо is highly overall correlated with Семестр and 1 other fieldsHigh correlation
Накоп отлично is highly overall correlated with отличноHigh correlation
Накоп зачет испр is highly overall correlated with зачет испр and 2 other fieldsHigh correlation
Накоп удовлетворительно испр is highly overall correlated with Накоп зачет до испр and 1 other fieldsHigh correlation
Накоп хорошо испр is highly overall correlated with Накоп удовлетворительно до испрHigh correlation
Накоп отлично испр is highly overall correlated with Накоп хорошо до испрHigh correlation
Накоп незачет до испр is highly overall correlated with незачет до испр and 2 other fieldsHigh correlation
Накоп зачет до испр is highly overall correlated with Накоп удовлетворительно испрHigh correlation
Накоп удовлетворительно до испр is highly overall correlated with Накоп хорошо испрHigh correlation
Накоп хорошо до испр is highly overall correlated with Накоп отлично испрHigh correlation
удовлетворительно испр is highly overall correlated with зачет испр and 4 other fieldsHigh correlation
хорошо испр is highly overall correlated with удовлетворительно до испрHigh correlation
отлично испр is highly overall correlated with хорошо до испрHigh correlation
удовлетворительно до испр is highly overall correlated with хорошо испрHigh correlation
хорошо до испр is highly overall correlated with отлично испрHigh correlation
удовлетворительно испр is highly imbalanced (90.0%)Imbalance
хорошо испр is highly imbalanced (79.8%)Imbalance
отлично испр is highly imbalanced (71.9%)Imbalance
зачет до испр is highly imbalanced (73.7%)Imbalance
удовлетворительно до испр is highly imbalanced (82.2%)Imbalance
хорошо до испр is highly imbalanced (80.0%)Imbalance
зачет испр is highly skewed (γ1 = 35.66660729)Skewed
незачет до испр is highly skewed (γ1 = 32.30947878)Skewed
Накоп зачет испр is highly skewed (γ1 = 35.01268132)Skewed
Накоп незачет до испр is highly skewed (γ1 = 28.09328313)Skewed
зачет has 268 (1.4%) zerosZeros
удовлетворительно has 11761 (61.7%) zerosZeros
хорошо has 6492 (34.0%) zerosZeros
отлично has 8566 (44.9%) zerosZeros
зачет испр has 19017 (99.7%) zerosZeros
незачет до испр has 18927 (99.3%) zerosZeros
Накоп удовлетворительно has 7824 (41.0%) zerosZeros
Накоп хорошо has 2602 (13.6%) zerosZeros
Накоп отлично has 4080 (21.4%) zerosZeros
Накоп зачет испр has 18889 (99.1%) zerosZeros
Накоп удовлетворительно испр has 17272 (90.6%) zerosZeros
Накоп хорошо испр has 14846 (77.9%) zerosZeros
Накоп отлично испр has 13575 (71.2%) zerosZeros
Накоп незачет до испр has 18583 (97.4%) zerosZeros
Накоп зачет до испр has 14085 (73.9%) zerosZeros
Накоп удовлетворительно до испр has 16267 (85.3%) zerosZeros
Накоп хорошо до испр has 15251 (80.0%) zerosZeros

Reproduction

Analysis started2023-10-06 06:17:02.404278
Analysis finished2023-10-06 06:18:00.453843
Duration58.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Семестр
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9649187
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:00.505804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4979464
Coefficient of variation (CV)0.63001201
Kurtosis-0.73880765
Mean3.9649187
Median Absolute Deviation (MAD)2
Skewness0.58517887
Sum75611
Variance6.2397363
MonotonicityNot monotonic
2023-10-06T13:18:00.598446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 3612
18.9%
2 3307
17.3%
3 3074
16.1%
4 1947
10.2%
5 1710
9.0%
6 1643
8.6%
7 1477
7.7%
8 1455
7.6%
9 465
 
2.4%
10 377
 
2.0%
ValueCountFrequency (%)
1 3612
18.9%
2 3307
17.3%
3 3074
16.1%
4 1947
10.2%
5 1710
9.0%
6 1643
8.6%
7 1477
7.7%
8 1455
7.6%
9 465
 
2.4%
10 377
 
2.0%
ValueCountFrequency (%)
11 3
 
< 0.1%
10 377
 
2.0%
9 465
 
2.4%
8 1455
7.6%
7 1477
7.7%
6 1643
8.6%
5 1710
9.0%
4 1947
10.2%
3 3074
16.1%
2 3307
17.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0
13794 
2
5148 
1
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19070
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 13794
72.3%
2 5148
 
27.0%
1 128
 
0.7%

Length

2023-10-06T13:18:00.694315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:00.826641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13794
72.3%
2 5148
 
27.0%
1 128
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 13794
72.3%
2 5148
 
27.0%
1 128
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19070
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13794
72.3%
2 5148
 
27.0%
1 128
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 19070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13794
72.3%
2 5148
 
27.0%
1 128
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13794
72.3%
2 5148
 
27.0%
1 128
 
0.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0
13606 
2
3496 
3
 
1053
1
 
915

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19070
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13606
71.3%
2 3496
 
18.3%
3 1053
 
5.5%
1 915
 
4.8%

Length

2023-10-06T13:18:00.922480image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:01.026342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13606
71.3%
2 3496
 
18.3%
3 1053
 
5.5%
1 915
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 13606
71.3%
2 3496
 
18.3%
3 1053
 
5.5%
1 915
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19070
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13606
71.3%
2 3496
 
18.3%
3 1053
 
5.5%
1 915
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 19070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13606
71.3%
2 3496
 
18.3%
3 1053
 
5.5%
1 915
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13606
71.3%
2 3496
 
18.3%
3 1053
 
5.5%
1 915
 
4.8%

незачет
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0
19070 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19070
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19070
100.0%

Length

2023-10-06T13:18:01.118249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:01.218116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19070
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19070
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19070
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19070
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19070
100.0%

зачет
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.995333
Minimum0
Maximum11
Zeros268
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:01.290020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.611625
Coefficient of variation (CV)0.4033769
Kurtosis0.33386917
Mean3.995333
Median Absolute Deviation (MAD)1
Skewness-0.018813269
Sum76191
Variance2.5973352
MonotonicityNot monotonic
2023-10-06T13:18:01.377902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 5509
28.9%
5 4070
21.3%
3 2749
14.4%
6 2392
12.5%
2 2251
11.8%
1 1087
 
5.7%
7 509
 
2.7%
0 268
 
1.4%
8 131
 
0.7%
10 50
 
0.3%
Other values (2) 54
 
0.3%
ValueCountFrequency (%)
0 268
 
1.4%
1 1087
 
5.7%
2 2251
11.8%
3 2749
14.4%
4 5509
28.9%
5 4070
21.3%
6 2392
12.5%
7 509
 
2.7%
8 131
 
0.7%
9 44
 
0.2%
ValueCountFrequency (%)
11 10
 
0.1%
10 50
 
0.3%
9 44
 
0.2%
8 131
 
0.7%
7 509
 
2.7%
6 2392
12.5%
5 4070
21.3%
4 5509
28.9%
3 2749
14.4%
2 2251
11.8%

удовлетворительно
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73392764
Minimum0
Maximum8
Zeros11761
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:01.469780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1227076
Coefficient of variation (CV)1.5297252
Kurtosis1.7115137
Mean0.73392764
Median Absolute Deviation (MAD)0
Skewness1.5360732
Sum13996
Variance1.2604724
MonotonicityNot monotonic
2023-10-06T13:18:01.572849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 11761
61.7%
1 3232
 
16.9%
2 2189
 
11.5%
3 1277
 
6.7%
4 522
 
2.7%
5 70
 
0.4%
6 17
 
0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 11761
61.7%
1 3232
 
16.9%
2 2189
 
11.5%
3 1277
 
6.7%
4 522
 
2.7%
5 70
 
0.4%
6 17
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 17
 
0.1%
5 70
 
0.4%
4 522
 
2.7%
3 1277
 
6.7%
2 2189
 
11.5%
1 3232
 
16.9%
0 11761
61.7%

хорошо
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2658626
Minimum0
Maximum11
Zeros6492
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:01.676709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2168533
Coefficient of variation (CV)0.96128384
Kurtosis1.0478301
Mean1.2658626
Median Absolute Deviation (MAD)1
Skewness0.8782534
Sum24140
Variance1.4807319
MonotonicityNot monotonic
2023-10-06T13:18:01.764592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 6492
34.0%
1 5291
27.7%
2 4126
21.6%
3 2243
 
11.8%
4 803
 
4.2%
5 80
 
0.4%
6 18
 
0.1%
9 6
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
0 6492
34.0%
1 5291
27.7%
2 4126
21.6%
3 2243
 
11.8%
4 803
 
4.2%
5 80
 
0.4%
6 18
 
0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 2
 
< 0.1%
9 6
 
< 0.1%
8 3
 
< 0.1%
7 4
 
< 0.1%
6 18
 
0.1%
5 80
 
0.4%
4 803
 
4.2%
3 2243
11.8%
2 4126
21.6%

отлично
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1577347
Minimum0
Maximum13
Zeros8566
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:01.852478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3521403
Coefficient of variation (CV)1.167919
Kurtosis2.1263301
Mean1.1577347
Median Absolute Deviation (MAD)1
Skewness1.1906029
Sum22078
Variance1.8282833
MonotonicityNot monotonic
2023-10-06T13:18:01.952339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8566
44.9%
1 4183
21.9%
2 2740
 
14.4%
3 2201
 
11.5%
4 1221
 
6.4%
5 114
 
0.6%
6 18
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
Other values (4) 10
 
0.1%
ValueCountFrequency (%)
0 8566
44.9%
1 4183
21.9%
2 2740
 
14.4%
3 2201
 
11.5%
4 1221
 
6.4%
5 114
 
0.6%
6 18
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 5
 
< 0.1%
7 8
 
< 0.1%
6 18
 
0.1%
5 114
 
0.6%
4 1221
6.4%

зачет испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0041426324
Minimum0
Maximum5
Zeros19017
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:02.036229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.098941043
Coefficient of variation (CV)23.883616
Kurtosis1531.9383
Mean0.0041426324
Median Absolute Deviation (MAD)0
Skewness35.666607
Sum79
Variance0.0097893299
MonotonicityNot monotonic
2023-10-06T13:18:02.128109image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19017
99.7%
1 43
 
0.2%
2 3
 
< 0.1%
5 3
 
< 0.1%
4 3
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 19017
99.7%
1 43
 
0.2%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 3
 
< 0.1%
3 1
 
< 0.1%
2 3
 
< 0.1%
1 43
 
0.2%
0 19017
99.7%

удовлетворительно испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0.0
18512 
1.0
 
529
2.0
 
28
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57210
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18512
97.1%
1.0 529
 
2.8%
2.0 28
 
0.1%
3.0 1
 
< 0.1%

Length

2023-10-06T13:18:02.228028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:02.330512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18512
97.1%
1.0 529
 
2.8%
2.0 28
 
0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 37582
65.7%
. 19070
33.3%
1 529
 
0.9%
2 28
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38140
66.7%
Other Punctuation 19070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37582
98.5%
1 529
 
1.4%
2 28
 
0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37582
65.7%
. 19070
33.3%
1 529
 
0.9%
2 28
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37582
65.7%
. 19070
33.3%
1 529
 
0.9%
2 28
 
< 0.1%
3 1
 
< 0.1%

хорошо испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0.0
17362 
1.0
 
1589
2.0
 
114
3.0
 
3
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57210
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 17362
91.0%
1.0 1589
 
8.3%
2.0 114
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-06T13:18:02.422387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:02.530244image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17362
91.0%
1.0 1589
 
8.3%
2.0 114
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36432
63.7%
. 19070
33.3%
1 1589
 
2.8%
2 114
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38140
66.7%
Other Punctuation 19070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36432
95.5%
1 1589
 
4.2%
2 114
 
0.3%
3 3
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36432
63.7%
. 19070
33.3%
1 1589
 
2.8%
2 114
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36432
63.7%
. 19070
33.3%
1 1589
 
2.8%
2 114
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

отлично испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0.0
16480 
1.0
2271 
2.0
 
303
3.0
 
14
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57210
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16480
86.4%
1.0 2271
 
11.9%
2.0 303
 
1.6%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Length

2023-10-06T13:18:02.626115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:02.733971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16480
86.4%
1.0 2271
 
11.9%
2.0 303
 
1.6%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 35550
62.1%
. 19070
33.3%
1 2271
 
4.0%
2 303
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38140
66.7%
Other Punctuation 19070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35550
93.2%
1 2271
 
6.0%
2 303
 
0.8%
3 14
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35550
62.1%
. 19070
33.3%
1 2271
 
4.0%
2 303
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35550
62.1%
. 19070
33.3%
1 2271
 
4.0%
2 303
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

незачет до испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010907184
Minimum0
Maximum8
Zeros18927
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:02.821856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18143578
Coefficient of variation (CV)16.634521
Kurtosis1301.1601
Mean0.010907184
Median Absolute Deviation (MAD)0
Skewness32.309479
Sum208
Variance0.032918942
MonotonicityNot monotonic
2023-10-06T13:18:02.913732image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 18927
99.3%
1 124
 
0.7%
2 9
 
< 0.1%
8 6
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 18927
99.3%
1 124
 
0.7%
2 9
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 6
 
< 0.1%
ValueCountFrequency (%)
8 6
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 9
 
< 0.1%
1 124
 
0.7%
0 18927
99.3%

зачет до испр
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0.0
17127 
1.0
1730 
2.0
 
211
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57210
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17127
89.8%
1.0 1730
 
9.1%
2.0 211
 
1.1%
3.0 2
 
< 0.1%

Length

2023-10-06T13:18:03.013605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:03.121425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17127
89.8%
1.0 1730
 
9.1%
2.0 211
 
1.1%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36197
63.3%
. 19070
33.3%
1 1730
 
3.0%
2 211
 
0.4%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38140
66.7%
Other Punctuation 19070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36197
94.9%
1 1730
 
4.5%
2 211
 
0.6%
3 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36197
63.3%
. 19070
33.3%
1 1730
 
3.0%
2 211
 
0.4%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36197
63.3%
. 19070
33.3%
1 1730
 
3.0%
2 211
 
0.4%
3 2
 
< 0.1%

удовлетворительно до испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0.0
17909 
1.0
 
1071
2.0
 
89
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57210
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 17909
93.9%
1.0 1071
 
5.6%
2.0 89
 
0.5%
3.0 1
 
< 0.1%

Length

2023-10-06T13:18:03.209338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:03.321158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17909
93.9%
1.0 1071
 
5.6%
2.0 89
 
0.5%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36979
64.6%
. 19070
33.3%
1 1071
 
1.9%
2 89
 
0.2%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38140
66.7%
Other Punctuation 19070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36979
97.0%
1 1071
 
2.8%
2 89
 
0.2%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36979
64.6%
. 19070
33.3%
1 1071
 
1.9%
2 89
 
0.2%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36979
64.6%
. 19070
33.3%
1 1071
 
1.9%
2 89
 
0.2%
3 1
 
< 0.1%

хорошо до испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0.0
17432 
1.0
 
1475
2.0
 
156
3.0
 
5
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57210
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17432
91.4%
1.0 1475
 
7.7%
2.0 156
 
0.8%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-06T13:18:03.417028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:03.520920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17432
91.4%
1.0 1475
 
7.7%
2.0 156
 
0.8%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36502
63.8%
. 19070
33.3%
1 1475
 
2.6%
2 156
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38140
66.7%
Other Punctuation 19070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36502
95.7%
1 1475
 
3.9%
2 156
 
0.4%
3 5
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36502
63.8%
. 19070
33.3%
1 1475
 
2.6%
2 156
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36502
63.8%
. 19070
33.3%
1 1475
 
2.6%
2 156
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Накоп незачет
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0.0
19070 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57210
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19070
100.0%

Length

2023-10-06T13:18:03.620784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:03.712665image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19070
100.0%

Most occurring characters

ValueCountFrequency (%)
0 38140
66.7%
. 19070
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38140
66.7%
Other Punctuation 19070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38140
100.0%
Other Punctuation
ValueCountFrequency (%)
. 19070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38140
66.7%
. 19070
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38140
66.7%
. 19070
33.3%

Накоп зачет
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.413214
Minimum0
Maximum69
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:03.808537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median14
Q327
95-th percentile39
Maximum69
Range69
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.826551
Coefficient of variation (CV)0.67917106
Kurtosis-0.63372732
Mean17.413214
Median Absolute Deviation (MAD)9
Skewness0.63884345
Sum332070
Variance139.86732
MonotonicityNot monotonic
2023-10-06T13:18:03.928379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1893
 
9.9%
8 1211
 
6.4%
10 1125
 
5.9%
11 1105
 
5.8%
9 800
 
4.2%
5 754
 
4.0%
15 644
 
3.4%
29 511
 
2.7%
13 488
 
2.6%
6 446
 
2.3%
Other values (59) 10093
52.9%
ValueCountFrequency (%)
0 13
 
0.1%
1 50
 
0.3%
2 292
 
1.5%
3 444
 
2.3%
4 1893
9.9%
5 754
 
4.0%
6 446
 
2.3%
7 406
 
2.1%
8 1211
6.4%
9 800
4.2%
ValueCountFrequency (%)
69 1
 
< 0.1%
67 2
< 0.1%
66 1
 
< 0.1%
65 1
 
< 0.1%
64 2
< 0.1%
63 1
 
< 0.1%
62 2
< 0.1%
61 1
 
< 0.1%
60 3
< 0.1%
59 1
 
< 0.1%

Накоп удовлетворительно
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7151023
Minimum0
Maximum58
Zeros7824
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:04.056176image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile15
Maximum58
Range58
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5561963
Coefficient of variation (CV)1.4955702
Kurtosis8.8330555
Mean3.7151023
Median Absolute Deviation (MAD)1
Skewness2.4256926
Sum70847
Variance30.871317
MonotonicityNot monotonic
2023-10-06T13:18:04.172021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7824
41.0%
1 2128
 
11.2%
2 1612
 
8.5%
3 1113
 
5.8%
4 941
 
4.9%
5 813
 
4.3%
6 620
 
3.3%
7 546
 
2.9%
8 489
 
2.6%
10 424
 
2.2%
Other values (44) 2560
 
13.4%
ValueCountFrequency (%)
0 7824
41.0%
1 2128
 
11.2%
2 1612
 
8.5%
3 1113
 
5.8%
4 941
 
4.9%
5 813
 
4.3%
6 620
 
3.3%
7 546
 
2.9%
8 489
 
2.6%
9 388
 
2.0%
ValueCountFrequency (%)
58 1
 
< 0.1%
56 1
 
< 0.1%
54 1
 
< 0.1%
52 1
 
< 0.1%
50 2
< 0.1%
49 2
< 0.1%
48 3
< 0.1%
46 3
< 0.1%
45 1
 
< 0.1%
44 3
< 0.1%

Накоп хорошо
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8439958
Minimum0
Maximum38
Zeros2602
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:04.291860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile16
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2905258
Coefficient of variation (CV)0.90529254
Kurtosis0.50393513
Mean5.8439958
Median Absolute Deviation (MAD)3
Skewness0.98693722
Sum111445
Variance27.989663
MonotonicityNot monotonic
2023-10-06T13:18:04.402788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 2602
13.6%
1 2127
11.2%
2 2005
10.5%
3 1706
 
8.9%
4 1291
 
6.8%
5 1114
 
5.8%
6 1070
 
5.6%
7 926
 
4.9%
8 858
 
4.5%
9 841
 
4.4%
Other values (26) 4530
23.8%
ValueCountFrequency (%)
0 2602
13.6%
1 2127
11.2%
2 2005
10.5%
3 1706
8.9%
4 1291
6.8%
5 1114
5.8%
6 1070
5.6%
7 926
 
4.9%
8 858
 
4.5%
9 841
 
4.4%
ValueCountFrequency (%)
38 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
32 1
 
< 0.1%
31 2
 
< 0.1%
30 1
 
< 0.1%
29 2
 
< 0.1%
28 4
< 0.1%
27 4
< 0.1%
26 7
< 0.1%

Накоп отлично
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3353435
Minimum0
Maximum42
Zeros4080
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:04.518632image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36
95-th percentile16
Maximum42
Range42
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.3576501
Coefficient of variation (CV)1.2358075
Kurtosis4.9169442
Mean4.3353435
Median Absolute Deviation (MAD)2
Skewness2.0743398
Sum82675
Variance28.704414
MonotonicityNot monotonic
2023-10-06T13:18:04.754318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 4080
21.4%
1 2988
15.7%
2 2631
13.8%
3 1993
10.5%
4 1229
 
6.4%
5 951
 
5.0%
6 943
 
4.9%
7 640
 
3.4%
8 603
 
3.2%
9 509
 
2.7%
Other values (30) 2503
13.1%
ValueCountFrequency (%)
0 4080
21.4%
1 2988
15.7%
2 2631
13.8%
3 1993
10.5%
4 1229
 
6.4%
5 951
 
5.0%
6 943
 
4.9%
7 640
 
3.4%
8 603
 
3.2%
9 509
 
2.7%
ValueCountFrequency (%)
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 4
 
< 0.1%
32 6
 
< 0.1%
31 8
< 0.1%
30 19
0.1%

Накоп зачет испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012899843
Minimum0
Maximum12
Zeros18889
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:04.846193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.189784
Coefficient of variation (CV)14.712118
Kurtosis1715.6126
Mean0.012899843
Median Absolute Deviation (MAD)0
Skewness35.012681
Sum246
Variance0.036017968
MonotonicityNot monotonic
2023-10-06T13:18:04.938070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18889
99.1%
1 156
 
0.8%
2 18
 
0.1%
5 3
 
< 0.1%
9 3
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
0 18889
99.1%
1 156
 
0.8%
2 18
 
0.1%
5 3
 
< 0.1%
9 3
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
9 3
 
< 0.1%
5 3
 
< 0.1%
2 18
 
0.1%
1 156
 
0.8%
0 18889
99.1%

Накоп удовлетворительно испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12669114
Minimum0
Maximum7
Zeros17272
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:05.025953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44733754
Coefficient of variation (CV)3.53093
Kurtosis26.709021
Mean0.12669114
Median Absolute Deviation (MAD)0
Skewness4.5856517
Sum2416
Variance0.20011087
MonotonicityNot monotonic
2023-10-06T13:18:05.109842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 17272
90.6%
1 1368
 
7.2%
2 278
 
1.5%
3 124
 
0.7%
4 22
 
0.1%
5 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 17272
90.6%
1 1368
 
7.2%
2 278
 
1.5%
3 124
 
0.7%
4 22
 
0.1%
5 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 5
 
< 0.1%
4 22
 
0.1%
3 124
 
0.7%
2 278
 
1.5%
1 1368
 
7.2%
0 17272
90.6%

Накоп хорошо испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34069219
Minimum0
Maximum7
Zeros14846
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:05.201715image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76197324
Coefficient of variation (CV)2.2365445
Kurtosis9.752315
Mean0.34069219
Median Absolute Deviation (MAD)0
Skewness2.8559608
Sum6497
Variance0.58060322
MonotonicityNot monotonic
2023-10-06T13:18:05.285608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14846
77.9%
1 2737
 
14.4%
2 957
 
5.0%
3 339
 
1.8%
4 139
 
0.7%
5 40
 
0.2%
6 11
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 14846
77.9%
1 2737
 
14.4%
2 957
 
5.0%
3 339
 
1.8%
4 139
 
0.7%
5 40
 
0.2%
6 11
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 11
 
0.1%
5 40
 
0.2%
4 139
 
0.7%
3 339
 
1.8%
2 957
 
5.0%
1 2737
 
14.4%
0 14846
77.9%

Накоп отлично испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46848453
Minimum0
Maximum8
Zeros13575
Zeros (%)71.2%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:05.385473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91309281
Coefficient of variation (CV)1.9490351
Kurtosis8.5530186
Mean0.46848453
Median Absolute Deviation (MAD)0
Skewness2.6078965
Sum8934
Variance0.83373849
MonotonicityNot monotonic
2023-10-06T13:18:05.473355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 13575
71.2%
1 3410
 
17.9%
2 1244
 
6.5%
3 514
 
2.7%
4 202
 
1.1%
5 82
 
0.4%
6 29
 
0.2%
7 10
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
0 13575
71.2%
1 3410
 
17.9%
2 1244
 
6.5%
3 514
 
2.7%
4 202
 
1.1%
5 82
 
0.4%
6 29
 
0.2%
7 10
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 10
 
0.1%
6 29
 
0.2%
5 82
 
0.4%
4 202
 
1.1%
3 514
 
2.7%
2 1244
 
6.5%
1 3410
 
17.9%
0 13575
71.2%

Накоп незачет до испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038699528
Minimum0
Maximum22
Zeros18583
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:05.569228image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37471053
Coefficient of variation (CV)9.6825606
Kurtosis1211.7637
Mean0.038699528
Median Absolute Deviation (MAD)0
Skewness28.093283
Sum738
Variance0.14040798
MonotonicityNot monotonic
2023-10-06T13:18:05.661074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 18583
97.4%
1 388
 
2.0%
2 64
 
0.3%
5 8
 
< 0.1%
6 7
 
< 0.1%
4 7
 
< 0.1%
3 6
 
< 0.1%
8 3
 
< 0.1%
16 3
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
0 18583
97.4%
1 388
 
2.0%
2 64
 
0.3%
3 6
 
< 0.1%
4 7
 
< 0.1%
5 8
 
< 0.1%
6 7
 
< 0.1%
8 3
 
< 0.1%
16 3
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
16 3
 
< 0.1%
8 3
 
< 0.1%
6 7
 
< 0.1%
5 8
 
< 0.1%
4 7
 
< 0.1%
3 6
 
< 0.1%
2 64
 
0.3%
1 388
 
2.0%
0 18583
97.4%

Накоп зачет до испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38830624
Minimum0
Maximum5
Zeros14085
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:05.748957image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78040372
Coefficient of variation (CV)2.0097635
Kurtosis7.1898762
Mean0.38830624
Median Absolute Deviation (MAD)0
Skewness2.5074727
Sum7405
Variance0.60902996
MonotonicityNot monotonic
2023-10-06T13:18:05.853636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 14085
73.9%
1 3403
 
17.8%
2 982
 
5.1%
3 408
 
2.1%
4 146
 
0.8%
5 46
 
0.2%
ValueCountFrequency (%)
0 14085
73.9%
1 3403
 
17.8%
2 982
 
5.1%
3 408
 
2.1%
4 146
 
0.8%
5 46
 
0.2%
ValueCountFrequency (%)
5 46
 
0.2%
4 146
 
0.8%
3 408
 
2.1%
2 982
 
5.1%
1 3403
 
17.8%
0 14085
73.9%

Накоп удовлетворительно до испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23434714
Minimum0
Maximum6
Zeros16267
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:05.981441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68477225
Coefficient of variation (CV)2.9220422
Kurtosis20.012483
Mean0.23434714
Median Absolute Deviation (MAD)0
Skewness4.0182332
Sum4469
Variance0.46891303
MonotonicityNot monotonic
2023-10-06T13:18:06.073317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16267
85.3%
1 1806
 
9.5%
2 616
 
3.2%
3 202
 
1.1%
4 98
 
0.5%
5 53
 
0.3%
6 28
 
0.1%
ValueCountFrequency (%)
0 16267
85.3%
1 1806
 
9.5%
2 616
 
3.2%
3 202
 
1.1%
4 98
 
0.5%
5 53
 
0.3%
6 28
 
0.1%
ValueCountFrequency (%)
6 28
 
0.1%
5 53
 
0.3%
4 98
 
0.5%
3 202
 
1.1%
2 616
 
3.2%
1 1806
 
9.5%
0 16267
85.3%

Накоп хорошо до испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28741479
Minimum0
Maximum7
Zeros15251
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size149.1 KiB
2023-10-06T13:18:06.181203image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68477137
Coefficient of variation (CV)2.3825196
Kurtosis14.951669
Mean0.28741479
Median Absolute Deviation (MAD)0
Skewness3.3085777
Sum5481
Variance0.46891182
MonotonicityNot monotonic
2023-10-06T13:18:06.269082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 15251
80.0%
1 2709
 
14.2%
2 758
 
4.0%
3 221
 
1.2%
4 91
 
0.5%
5 17
 
0.1%
6 17
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
0 15251
80.0%
1 2709
 
14.2%
2 758
 
4.0%
3 221
 
1.2%
4 91
 
0.5%
5 17
 
0.1%
6 17
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
7 6
 
< 0.1%
6 17
 
0.1%
5 17
 
0.1%
4 91
 
0.5%
3 221
 
1.2%
2 758
 
4.0%
1 2709
 
14.2%
0 15251
80.0%

отчислен
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
0
16805 
1
2265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19070
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16805
88.1%
1 2265
 
11.9%

Length

2023-10-06T13:18:06.371266image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T13:18:06.475094image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 16805
88.1%
1 2265
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 16805
88.1%
1 2265
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19070
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16805
88.1%
1 2265
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 19070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16805
88.1%
1 2265
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16805
88.1%
1 2265
 
11.9%

Interactions

2023-10-06T13:17:57.406543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:05.940902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:09.206153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:13.512035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:16.733117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:19.528842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:22.332812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:24.977399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:27.448179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:29.912367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:32.024199image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:34.693049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:37.454920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:41.946279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:45.733473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:48.054997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:50.410500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:52.779082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:55.127773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:57.510408image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:06.100654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:09.428660image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:13.631903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:16.852929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:19.648715image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:22.460660image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:25.093248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:27.545608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:30.020221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:32.124066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:34.852806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:37.576138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:42.146009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:45.833340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:48.170812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:50.584748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:52.874954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:55.235628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T13:17:57.618262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-06T13:17:09.866834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-06T13:17:40.307261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-06T13:17:47.256013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-06T13:17:51.919860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-06T13:17:56.511359image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-06T13:17:52.673047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-06T13:17:57.282714image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-06T13:18:06.578954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Семестрзачетудовлетворительнохорошоотличнозачет испрнезачет до испрНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испрФорма обученияКвалификацияудовлетворительно испрхорошо испротлично испрзачет до испрудовлетворительно до испрхорошо до испротчислен
Семестр1.000-0.0500.001-0.031-0.022-0.035-0.0510.9320.4360.7030.4740.0230.2860.4730.4250.0530.5000.3960.3290.2350.2690.0840.1000.1030.1620.1170.0790.217
зачет-0.0501.0000.0120.1870.135-0.045-0.0340.189-0.0150.1290.073-0.071-0.071-0.0270.004-0.0710.016-0.043-0.0070.2350.3240.0760.0540.0760.1290.0390.0630.061
удовлетворительно0.0010.0121.000-0.085-0.511-0.008-0.0080.0270.6790.014-0.4720.0240.1710.080-0.2020.021-0.0270.144-0.1570.2580.2020.0770.0550.0830.0200.0820.0630.238
хорошо-0.0310.187-0.0851.000-0.309-0.025-0.0270.0340.0700.455-0.199-0.0330.0090.047-0.043-0.0300.006-0.0050.0030.0590.1360.0050.0150.0470.0400.0500.0340.013
отлично-0.0220.135-0.511-0.3091.0000.009-0.0060.028-0.492-0.1500.679-0.010-0.169-0.1610.133-0.0280.035-0.2120.0440.2300.1950.0440.0590.0480.1070.0880.0490.174
зачет испр-0.035-0.045-0.008-0.0250.0091.0000.608-0.048-0.025-0.041-0.0090.5400.0350.0120.0070.329-0.022-0.0140.0010.0180.0550.5980.4120.0000.0000.0000.0000.051
незачет до испр-0.051-0.034-0.008-0.027-0.0060.6081.000-0.055-0.023-0.055-0.0250.3370.0840.0190.0270.538-0.031-0.0170.0000.0160.0610.6120.4090.0300.0000.0000.0000.059
Накоп зачет0.9320.1890.0270.0340.028-0.048-0.0551.0000.4480.7460.4850.0050.2500.4460.4260.0410.4890.3690.3260.1530.3250.0720.1000.0850.1550.1060.0590.210
Накоп удовлетворительно0.436-0.0150.6790.070-0.492-0.025-0.0230.4481.0000.408-0.3080.0320.3530.358-0.0090.0510.2250.3680.0200.2700.1860.0950.0770.0340.0510.0920.0230.083
Накоп хорошо0.7030.1290.0140.455-0.150-0.041-0.0550.7460.4081.0000.2310.0050.2440.4290.3040.0260.4040.3190.2790.1160.2850.0530.0940.0510.1070.0810.0420.130
Накоп отлично0.4740.073-0.472-0.1990.679-0.009-0.0250.485-0.3080.2311.0000.004-0.0250.1000.4040.0040.311-0.0010.2780.1410.1520.0250.0250.0860.0800.0340.0440.195
Накоп зачет испр0.023-0.0710.024-0.033-0.0100.5400.3370.0050.0320.0050.0041.0000.0800.0570.0110.609-0.0150.0300.0080.0180.0480.5950.4090.0000.0000.0000.0000.051
Накоп удовлетворительно испр0.286-0.0710.1710.009-0.1690.0350.0840.2500.3530.244-0.0250.0801.0000.1850.0080.1950.5050.192-0.0040.1360.1000.6730.0640.0050.1730.0730.0000.020
Накоп хорошо испр0.473-0.0270.0800.047-0.1610.0120.0190.4460.3580.4290.1000.0570.1851.0000.2270.1150.4750.7330.2010.1210.1310.0200.3500.0380.1350.2870.0290.099
Накоп отлично испр0.4250.004-0.202-0.0430.1330.0070.0270.426-0.0090.3040.4040.0110.0080.2271.0000.0780.4010.2270.8130.0680.0990.0250.0240.3790.1320.0660.3050.168
Накоп незачет до испр0.053-0.0710.021-0.030-0.0280.3290.5380.0410.0510.0260.0040.6090.1950.1150.0781.000-0.0300.0910.0450.0260.0480.5960.4160.0000.0000.0000.0000.053
Накоп зачет до испр0.5000.016-0.0270.0060.035-0.022-0.0310.4890.2250.4040.311-0.0150.5050.4750.401-0.0301.0000.1590.1390.0870.1710.1740.1500.1420.3890.0580.0250.115
Накоп удовлетворительно до испр0.396-0.0430.144-0.005-0.212-0.014-0.0170.3690.3680.319-0.0010.0300.1920.7330.2270.0910.1591.0000.1710.1650.1100.0000.2310.0380.0250.3830.0160.093
Накоп хорошо до испр0.329-0.007-0.1570.0030.0440.0010.0000.3260.0200.2790.2780.008-0.0040.2010.8130.0450.1390.1711.0000.0450.0830.0210.0130.3430.0090.0430.4120.143
Форма обучения0.2350.2350.2580.0590.2300.0180.0160.1530.2700.1160.1410.0180.1360.1210.0680.0260.0870.1650.0451.0000.2880.0670.0450.0730.0240.0800.0610.138
Квалификация0.2690.3240.2020.1360.1950.0550.0610.3250.1860.2850.1520.0480.1000.1310.0990.0480.1710.1100.0830.2881.0000.0500.0650.0960.1010.0550.0790.135
удовлетворительно испр0.0840.0760.0770.0050.0440.5980.6120.0720.0950.0530.0250.5950.6730.0200.0250.5960.1740.0000.0210.0670.0501.0000.0000.0270.3180.0000.0220.030
хорошо испр0.1000.0540.0550.0150.0590.4120.4090.1000.0770.0940.0250.4090.0640.3500.0240.4160.1500.2310.0130.0450.0650.0001.0000.0150.3190.6410.0090.051
отлично испр0.1030.0760.0830.0470.0480.0000.0300.0850.0340.0510.0860.0000.0050.0380.3790.0000.1420.0380.3430.0730.0960.0270.0151.0000.2690.0520.7780.101
зачет до испр0.1620.1290.0200.0400.1070.0000.0000.1550.0510.1070.0800.0000.1730.1350.1320.0000.3890.0250.0090.0240.1010.3180.3190.2691.0000.0210.0300.049
удовлетворительно до испр0.1170.0390.0820.0500.0880.0000.0000.1060.0920.0810.0340.0000.0730.2870.0660.0000.0580.3830.0430.0800.0550.0000.6410.0520.0211.0000.0000.045
хорошо до испр0.0790.0630.0630.0340.0490.0000.0000.0590.0230.0420.0440.0000.0000.0290.3050.0000.0250.0160.4120.0610.0790.0220.0090.7780.0300.0001.0000.080
отчислен0.2170.0610.2380.0130.1740.0510.0590.2100.0830.1300.1950.0510.0200.0990.1680.0530.1150.0930.1430.1380.1350.0300.0510.1010.0490.0450.0801.000

Missing values

2023-10-06T13:17:59.764355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-06T13:18:00.187462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

СеместрФорма обученияКвалификациянезачетзачетудовлетворительнохорошоотличнозачет испрудовлетворительно испрхорошо испротлично испрнезачет до испрзачет до испрудовлетворительно до испрхорошо до испрНакоп незачетНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испротчислен
012006.00.01.01.00.00.00.00.00.00.00.00.00.06.00.01.01.00.00.00.00.00.00.00.00.00
122005.01.00.02.00.00.01.00.00.00.01.00.00.011.01.01.03.00.00.01.00.00.00.01.00.00
232002.00.02.01.00.00.01.00.00.00.01.00.00.013.01.03.04.00.00.02.00.00.00.02.00.00
342004.00.00.02.00.00.00.01.00.00.01.00.00.017.01.03.06.00.00.02.01.00.00.03.00.00
452005.00.02.00.00.00.01.00.00.00.01.00.00.022.01.05.06.00.00.03.01.00.00.04.00.00
562004.00.02.00.00.00.01.00.00.00.01.00.00.026.01.07.06.00.00.04.01.00.00.05.00.00
672003.00.03.00.00.00.01.00.00.00.01.00.00.029.01.010.06.00.00.05.01.00.00.06.00.00
782005.00.01.01.00.00.00.01.00.00.00.01.00.034.01.011.07.00.00.05.02.00.00.06.01.00
892003.00.02.00.00.00.00.00.00.00.00.00.00.037.01.013.07.00.00.05.02.00.00.06.01.00
9102001.00.00.01.00.00.00.01.00.00.00.01.00.038.01.013.08.00.00.05.03.00.00.06.02.00
СеместрФорма обученияКвалификациянезачетзачетудовлетворительнохорошоотличнозачет испрудовлетворительно испрхорошо испротлично испрнезачет до испрзачет до испрудовлетворительно до испрхорошо до испрНакоп незачетНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испротчислен
1906052003.00.03.01.00.00.00.00.00.00.00.00.00.015.01.012.05.00.00.00.00.00.00.00.00.00
1906162005.01.01.01.00.00.00.00.00.00.00.00.00.020.02.013.06.00.00.00.00.00.00.00.00.00
1906272004.03.00.00.00.00.00.00.00.00.00.00.00.024.05.013.06.00.00.00.00.00.00.00.00.00
1906382004.00.02.01.00.00.00.00.00.00.00.00.00.028.05.015.07.00.00.00.00.00.00.00.00.00
1906492000.03.02.00.00.00.00.00.00.00.00.00.00.028.08.017.07.00.00.00.00.00.00.00.00.00
19065102000.02.00.00.00.00.01.00.00.01.00.00.00.028.010.017.07.00.00.01.00.00.01.00.00.00
1906612003.01.04.00.00.00.00.00.00.00.00.00.00.03.01.04.00.00.00.00.00.00.00.00.00.01
1906712004.01.03.00.00.00.00.00.00.00.00.00.00.07.02.07.00.00.00.00.00.00.00.00.00.01
1906822005.01.03.00.00.00.00.00.00.00.00.00.00.012.03.010.00.00.00.00.00.00.00.00.00.01
1906922005.02.01.00.00.00.00.00.00.00.00.00.00.017.05.011.00.00.00.00.00.00.00.00.00.01

Duplicate rows

Most frequently occurring

СеместрФорма обученияКвалификациянезачетзачетудовлетворительнохорошоотличнозачет испрудовлетворительно испрхорошо испротлично испрнезачет до испрзачет до испрудовлетворительно до испрхорошо до испрНакоп незачетНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испротчислен# duplicates
16710204.00.00.03.00.00.00.00.00.00.00.00.00.04.00.00.03.00.00.00.00.00.00.00.00.00232
21810302.00.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00166
56820204.00.00.03.00.00.00.00.00.00.00.00.00.08.00.00.06.00.00.00.00.00.00.00.00.00128
17410204.00.01.02.00.00.00.00.00.00.00.00.00.04.00.01.02.00.00.00.00.00.00.00.00.00118
67520302.00.00.02.00.00.00.00.00.00.00.00.00.04.00.00.02.00.00.00.00.00.00.00.00.0095
103930304.00.00.00.00.00.00.00.00.00.00.00.00.08.00.00.02.00.00.00.00.00.00.00.00.0092
116040302.00.00.01.00.00.00.00.00.00.00.00.00.010.00.00.03.00.00.00.00.00.00.00.00.0080
18310204.00.02.01.00.00.00.00.00.00.00.00.00.04.00.02.01.00.00.00.00.00.00.00.00.0074
1810004.00.03.01.00.00.00.00.00.00.00.00.00.04.00.03.01.00.00.00.00.00.00.00.00.0068
123250301.00.00.00.00.00.00.00.00.00.00.00.00.011.00.00.03.00.00.00.00.00.00.00.00.0055